ith weather data acquisition. Edited by: Filipe Matias , University of Wisconsin-Madison, United States Reviewed by: Xiaolong Yang , Nantong University, China David Mojaravscki , State University of Campinas, Brazil Data availability statement The datasets presented in this study can be found in online repositories, on Figshare https://figshare.com/s/2765f89c7ea840e5c6be?file=59367320 . The names of the repository/repositories and accessionnumber(s) can be found in the article/ Supplementary Material . Author contributions BG: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data
Open resource ↗Figshare · lines:471-515Unverified paper record
Modeling grain biochemical composition traits of commercial sorghum hybrids under diverse management practices.
Frontiers in plant science · 16 Feb 2026 · 10.3389/fpls.2026.1768456
Abstract
Introduction Sorghum ( Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Plant phenotyping relevance
穀粒の生化学的形質を少数の測定値から推定する機械学習モデルの開発・検証が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractreducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately
abstractWe used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices
abstractAmong ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92).
Code and data availability
The article's data availability statement points to a Figshare deposit containing the study's datasets (agronomic, physiological, UAV-based, and grain composition data used for ML modeling). No author analysis code or trained model checkpoints are explicitly deposited.
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